Image representation learning is an essential foundation for various computer vision tasks. Especially, self-supervised learning is one of the most thrilling branches in this field. Self-supervised learning encourages the machine to learn image representation from a pretext task, which is able to automatically generate a supervision signal via a predefined transformation without any human labelling. For example, image permutation and rotation prediction [1]}, [2]} help the model learn the image feature representation.
